A carbon emission scheduling method, apparatus, equipment and medium

Abstract

This invention discloses a carbon emission scheduling method, apparatus, equipment, and medium, belonging to the field of power systems. The method involves: acquiring real-time monitoring data, historical carbon emission data, and production efficiency data for each management area in a city, and calculating the regional fairness contribution value and regional efficiency index score respectively; then, constructing a multi-objective optimization model with the goal of maximizing the weighted sum of fairness and efficiency, and solving for the initial carbon emission budget allocation ratio; next, determining the comprehensive adjustment coefficient based on the production efficiency data, and determining the carbon emission budget elasticity correction amount based on the real-time monitoring data; finally, combining the initial allocation ratio, comprehensive adjustment coefficient, and elasticity correction amount to generate the carbon emission budget for each area, and scheduling each carbon emission unit. Therefore, by implementing this invention, the problem of insufficient accuracy in carbon emission scheduling in existing technologies can be solved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a carbon emission dispatching method, apparatus, equipment, and medium. Background Technology

[0002] Carbon emission scheduling is a core and crucial element in the refined management of carbon emission budgets, and an important means of coordinating regional economic development with carbon reduction responsibilities. On the one hand, different management regions exhibit significant differences in population, GDP, industrial structure, and clean energy development levels, necessitating scientific carbon emission scheduling to achieve a reasonable allocation of carbon budgets across regions, balancing the fairness of development with the efficiency of emission reduction. On the other hand, total carbon emissions are dynamically affected by energy consumption factors such as electricity consumption, and economic and energy development is subject to numerous uncertainties. Only through dynamic scheduling can actual changes be adapted. Furthermore, precise scheduling and control are required during the execution of carbon emission budgets to track deviations and intervene in a timely manner, ensuring the achievement of carbon reduction targets and improving the controllability and transparency of carbon emission management.

[0003] Existing carbon emission dispatching technologies are mostly based on static initial budget allocation. Some methods conduct fairness-oriented allocation based on data such as population, GDP, and historical carbon emissions, while others conduct efficiency-oriented allocation based on indicators such as carbon emission intensity per unit of industrial added value and carbon productivity. These technologies have several significant shortcomings in terms of dispatching accuracy: First, it is difficult to balance fairness and efficiency in the allocation process. Calculations based on a single indicator can easily lead to allocation results that are out of sync with actual regional development and emission reduction capabilities, resulting in insufficient accuracy in initial dispatching. Second, they do not calculate the carbon emission elasticity coefficient of electricity, making it impossible to accurately quantify the actual impact of electricity consumption fluctuations on carbon emissions. Dynamic corrections lack data support, resulting in delayed responses and low correction accuracy. Summary of the Invention

[0004] This invention provides a carbon emission scheduling method, apparatus, equipment, and medium that can solve the problem of insufficient accuracy in carbon emission scheduling in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a carbon emission scheduling method, comprising: Obtain real-time monitoring data for each management area in the city; Based on the historical carbon emission data corresponding to each management area, the regional equity contribution value corresponding to each management area is calculated, and based on the production efficiency data corresponding to each management area, the regional efficiency index score corresponding to each management area is obtained. Based on the fairness contribution value and efficiency index score of each region, a multi-objective optimization model is established with the goal of maximizing the weighted sum of the fairness contribution value and the efficiency index score of each region. The multi-objective optimization model is then solved to obtain the initial carbon emission budget allocation ratio corresponding to each management region. Based on the production efficiency data, determine the comprehensive adjustment coefficient corresponding to each management area, and based on the real-time monitoring data, determine the carbon emission budget elasticity adjustment amount corresponding to each management area. Based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elasticity adjustment amount, the carbon emission budget corresponding to each management area is obtained, and carbon emission scheduling is carried out for each carbon emission unit in each management area according to the carbon emission budget.

[0006] This application's embodiments acquire historical carbon emission data and production efficiency data, and calculate fairness contribution values ​​and efficiency index scores using the Shapley value method and entropy value method respectively. This achieves a quantitative separation of fairness and efficiency dimensions, providing a dual benchmark for subsequent optimization that balances historical responsibility and development weight, avoiding bias caused by single-indicator allocation. Secondly, a multi-objective optimization model is constructed with the goal of maximizing the weighted average of fairness and efficiency, and multiple constraints are introduced to ensure that the initial budget allocation ratio achieves a game-theoretic equilibrium between fairness and efficiency while meeting regional carrying capacity, thus improving the scientific rigor of the initial scheme. Based on this, and according to production efficiency data... The system calculates a comprehensive adjustment coefficient to incentivize or constrain the initial budget, dynamically linking the budget amount to the region's actual emission reduction efforts. Simultaneously, it determines the flexible adjustment amount for the carbon emission budget based on real-time monitoring data. By quantifying the correlation between electricity consumption fluctuations and carbon emissions, it incorporates uncertainties in power system operation into the adjustment scope, enabling the budget to dynamically adjust in response to changes in actual energy consumption scenarios. Finally, it integrates the initial allocation ratio, the comprehensive adjustment coefficient, and the flexible adjustment amount to generate the final carbon emission budget, and uses this budget to schedule each carbon emission unit. This achieves a shift from static quota allocation to dynamic flexible response, ensuring the accuracy of carbon emission scheduling from multiple dimensions.

[0007] As a preferred example of the first aspect, the calculation of the regional equity contribution value corresponding to each of the management areas based on the historical carbon emission data corresponding to each management area includes: Each management region is assigned a set of management regions. Based on the management regions and historical carbon emission data, the Shapley value calculation formula is used to calculate the regional fairness contribution value for each management region. In each calculation, the marginal contribution value of each subset in the management region set corresponding to the currently calculated management region is obtained based on the currently calculated management region and the historical carbon emission data corresponding to the currently calculated management region. The marginal contribution values ​​are then weighted and summed to obtain the regional fairness contribution value for the currently calculated management region.

[0008] In this preferred example, the Shapley value method is used to calculate the regional fairness contribution value. This quantifies and weights the marginal contributions of each subset within the management area set, thereby scientifically measuring the actual responsibility and contribution of each region in historical carbon emissions. This approach solves the problem of objectively quantifying fairness, avoids biases caused by single-indicator allocation, and provides an accurate and interpretable fairness quantification benchmark for subsequently constructing a multi-objective optimization model that balances fairness and efficiency. This ensures the rationality and scientific nature of the initial allocation of the carbon emission budget.

[0009] As a preferred example of the first aspect, the step of obtaining the regional efficiency index score corresponding to each of the management regions based on the production efficiency data corresponding to each management region includes: Each time a calculation is performed, the production efficiency data for the current calculation is standardized to obtain the standardized production efficiency data for the current calculation. Based on the preset set of evaluation indicators, the entropy value calculation formula is used to calculate the weight of each evaluation indicator in the preset set of evaluation indicators for the current management area. By combining the currently calculated standardized production efficiency data with the weights of each indicator, the regional efficiency index score corresponding to the currently calculated management area is obtained.

[0010] As a preferred example of the first aspect, determining the comprehensive adjustment coefficient corresponding to each of the management areas based on the production efficiency data includes: The production efficiency data are divided into positive efficiency index data and negative efficiency index data. The positive efficiency index data is standardized to obtain positive standardized data, and the negative efficiency index data is standardized using a second preset standardization formula to obtain negative standardized data. Based on the weight of each indicator, the positive standardized data, and the negative standardized data, the comprehensive adjustment coefficient corresponding to each management region is determined.

[0011] In this preferred example, by dividing production efficiency data into positive and negative indicators and standardizing them separately, the problem of difficulty in comprehensive comparison caused by inconsistent indicator dimensions and directions is solved. Positive indicators are standardized positively, and negative indicators are standardized negatively, ensuring that all indicators are uniformly oriented towards higher values, thus guaranteeing data comparability and reasonable synthesis. Furthermore, by combining the weight of each indicator with the standardized data, adjustment coefficients for each region are calculated, enabling dynamic quantitative assessment of regional emission reduction performance. This solves the technical problem that initial budget allocation cannot reflect the actual efforts and efficiency differences between regions, providing a scientific basis for the dynamic adjustment of subsequent carbon emission budgets.

[0012] As a preferred example of the first aspect, the establishment of a multi-objective optimization model based on the fairness contribution value and the efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the regional efficiency index score as the optimization objective, includes: Based on the fairness contribution value and efficiency index score of each region, an objective function is established with the goal of maximizing the weighted sum of Shapley value weight and entropy value weight. Based on the fairness contribution value of each region and the efficiency index score of each region, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value and minimum entropy. The multi-objective optimization model is established based on the objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, and the minimum entropy constraint.

[0013] In this preferred example, by using the weighted sum of the Shapley value weight and the entropy value weight as the objective function, this approach achieves synergistic optimization of fairness and efficiency, resolving the problem of neglecting one aspect for another caused by the allocation of a single indicator. By introducing multiple constraints such as non-negativity of weights, summation, upper and lower limits, Shapley value proportionality, and minimum entropy, the rationality, stability, and interpretability of the weight allocation are ensured, avoiding extreme allocation results that deviate from the actual carrying capacity of the region. The resulting multi-objective optimization model can accurately solve the initial carbon emission budget allocation ratio in a game that balances historical responsibility and development efficiency, solving the technical challenge of insufficient accuracy in initial scheduling.

[0014] As a preferred example of the first aspect, determining the carbon emission budget flexibility adjustment amount corresponding to each of the management areas based on the real-time monitoring data includes: Based on the real-time monitoring data, the baseline carbon emissions, and the electricity carbon emission elasticity coefficients, the carbon emission budget elasticity adjustment amount corresponding to each management area is determined; wherein, the baseline carbon emissions are determined based on a preset carbon emission measurement device, and the electricity carbon emission elasticity coefficients are determined based on the baseline carbon emissions.

[0015] In this preferred example, by introducing an elastic correction mechanism based on real-time monitoring data, baseline carbon emissions, and the electricity carbon emission elasticity coefficient, this approach solves the problem of traditional static scheduling failing to respond to fluctuations in electricity consumption, leading to correction lags. Specifically, a baseline carbon emission is accurately determined using carbon emission measurement devices, and the electricity carbon emission elasticity coefficient is calculated accordingly, quantifying the dynamic correlation between electricity consumption fluctuations and carbon emissions. Then, combined with real-time monitoring data, the elastic correction amount is calculated, enabling dynamic adaptation of the carbon emission budget to changes in actual energy consumption scenarios. This approach overcomes the technical shortcomings of dynamic correction, such as lack of data support, response lag, and low correction accuracy, providing a reliable basis for accurate dynamic scheduling of the carbon emission budget.

[0016] As a preferred example of the first aspect, the step of obtaining the carbon emission budget corresponding to each management area based on each of the initial carbon emission budget allocation ratios, each of the comprehensive adjustment coefficients, and each of the carbon emission budget elasticity correction amounts includes: Each of the initial carbon emission budget allocation ratios is multiplied by the comprehensive adjustment coefficient to obtain the first multiplier value corresponding to each of the management areas. Each of the first multipliers is then multiplied by the carbon emission budget elasticity adjustment amount to obtain the carbon emission budget corresponding to each of the management areas.

[0017] In a second aspect, the present invention provides a carbon emission scheduling device, comprising: a data acquisition module, a first scheduling module, a second scheduling module, a third scheduling module, and a fourth scheduling module; The data acquisition module is used to acquire real-time monitoring data corresponding to each management area in the city; The first scheduling module is used to calculate the regional fairness contribution value of each management area based on the historical carbon emission data of each management area, and to obtain the regional efficiency index score of each management area based on the production efficiency data of each management area. The second scheduling module is used to establish a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the efficiency index score of each region as the optimization objective, and solve the multi-objective optimization model to obtain the initial carbon emission budget allocation ratio corresponding to each management region. The third scheduling module is used to determine the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data, and to determine the carbon emission budget elasticity correction amount corresponding to each management area based on the real-time monitoring data. The fourth scheduling module is used to obtain the carbon emission budget corresponding to each management area based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elastic correction amount, and to perform carbon emission scheduling on each carbon emission unit in each management area according to the carbon emission budget.

[0018] As a preferred example of the second aspect, the calculation of the regional equity contribution value corresponding to each of the management areas based on the historical carbon emission data of each management area includes: Each management region is assigned a set of management regions. Based on the management regions and historical carbon emission data, the Shapley value calculation formula is used to calculate the regional fairness contribution value for each management region. In each calculation, the marginal contribution value of each subset in the management region set corresponding to the currently calculated management region is obtained based on the currently calculated management region and the historical carbon emission data corresponding to the currently calculated management region. The marginal contribution values ​​are then weighted and summed to obtain the regional fairness contribution value for the currently calculated management region.

[0019] As a preferred example of the second aspect, the step of obtaining the regional efficiency index score corresponding to each of the management regions based on the production efficiency data corresponding to each management region includes: Each time a calculation is performed, the production efficiency data for the current calculation is standardized to obtain the standardized production efficiency data for the current calculation. Based on the preset set of evaluation indicators, the entropy value calculation formula is used to calculate the weight of each evaluation indicator in the preset set of evaluation indicators for the current management area. By combining the currently calculated standardized production efficiency data with the weights of each indicator, the regional efficiency index score corresponding to the currently calculated management area is obtained.

[0020] As a preferred example of the second aspect, determining the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data includes: The production efficiency data are divided into positive efficiency index data and negative efficiency index data. The positive efficiency index data is standardized to obtain positive standardized data, and the negative efficiency index data is standardized using a second preset standardization formula to obtain negative standardized data. Based on the weight of each indicator, the positive standardized data, and the negative standardized data, the comprehensive adjustment coefficient corresponding to each management region is determined.

[0021] As a preferred example of the second aspect, the establishment of a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the regional efficiency index score as the optimization objective, includes: Based on the fairness contribution value and efficiency index score of each region, an objective function is established with the goal of maximizing the weighted sum of Shapley value weight and entropy value weight. Based on the fairness contribution value of each region and the efficiency index score of each region, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value and minimum entropy. The multi-objective optimization model is established based on the objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, and the minimum entropy constraint.

[0022] As a preferred example of the second aspect, determining the carbon emission budget flexibility adjustment amount corresponding to each of the management areas based on the real-time monitoring data includes: Based on the real-time monitoring data, the baseline carbon emissions, and the electricity carbon emission elasticity coefficients, the carbon emission budget elasticity adjustment amount corresponding to each management area is determined; wherein, the baseline carbon emissions are determined based on a preset carbon emission measurement device, and the electricity carbon emission elasticity coefficients are determined based on the baseline carbon emissions.

[0023] As a preferred example of the second aspect, the step of obtaining the carbon emission budget corresponding to each management area based on each of the initial carbon emission budget allocation ratios, each of the comprehensive adjustment coefficients, and each of the carbon emission budget elasticity correction amounts includes: Each of the initial carbon emission budget allocation ratios is multiplied by the comprehensive adjustment coefficient to obtain the first multiplier value corresponding to each of the management areas. Each of the first multipliers is then multiplied by the carbon emission budget elasticity adjustment amount to obtain the carbon emission budget corresponding to each of the management areas.

[0024] In summary, this application's embodiments, by acquiring historical carbon emission data and production efficiency data, calculate fairness contribution values ​​and efficiency index scores using the Shapley value method and entropy value method respectively, achieving a quantitative separation of fairness and efficiency dimensions. This provides a dual benchmark for subsequent optimization, taking into account both historical responsibility and development weight, avoiding biases caused by single-indicator allocation. Secondly, a multi-objective optimization model is constructed with the goal of maximizing the weighted average of fairness and efficiency, and multiple constraints are introduced to ensure that the initial budget allocation ratio achieves a game equilibrium between fairness and efficiency while meeting regional carrying capacity, thus improving the scientific nature of the initial scheme. Based on this, according to production efficiency data... The comprehensive adjustment coefficient is calculated to incentivize or constrain the initial budget, dynamically linking the budget amount to the region's actual emission reduction efforts. Simultaneously, the flexible adjustment amount for the carbon emission budget is determined based on real-time monitoring data. By quantifying the correlation between electricity consumption fluctuations and carbon emissions, uncertainties in power system operation are incorporated into the adjustment scope, enabling the budget to dynamically adjust in response to changes in actual energy consumption scenarios. Finally, the initial allocation ratio, comprehensive adjustment coefficient, and flexible adjustment amount are integrated to generate the final carbon emission budget, which is then used to schedule each carbon emission unit. This achieves a shift from static quota allocation to dynamic flexible response, ensuring the accuracy of carbon emission scheduling from multiple dimensions.

[0025] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the carbon emission scheduling method of the present invention.

[0026] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps as described in the carbon emission scheduling method of the present invention. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart of an embodiment of a carbon emission scheduling method provided by the present invention; Figure 2 This is a module structure diagram of one embodiment of a carbon emission scheduling device provided by the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0031] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0034] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0035] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0036] Example 1 See Figure 1 To address the problem of insufficient accuracy in carbon emission scheduling in existing technologies, an embodiment of the present invention provides a carbon emission scheduling method, comprising: S1. Obtain real-time monitoring data corresponding to each management area in the city; Specifically, the acquisition of real-time monitoring data corresponding to each management area in the city can be implemented through the following preferred scheme: First, set a monitoring cycle for budget execution, with monitoring conducted monthly or quarterly. Then, at the end of each monitoring cycle, collect data on the actual carbon emissions of each region during that cycle as real-time monitoring data.

[0037] S2. Based on the historical carbon emission data corresponding to each management area, calculate the regional equity contribution value corresponding to each management area, and based on the production efficiency data corresponding to each management area, obtain the regional efficiency index score corresponding to each management area. As a preferred implementation, the step of calculating the regional equity contribution value corresponding to each management area based on the historical carbon emission data corresponding to each management area includes: Each management region is assigned a set of management regions. Based on the management regions and historical carbon emission data, the Shapley value calculation formula is used to calculate the regional fairness contribution value for each management region. In each calculation, the marginal contribution value of each subset in the management region set corresponding to the currently calculated management region is obtained based on the currently calculated management region and the historical carbon emission data corresponding to the currently calculated management region. The marginal contribution values ​​are then weighted and summed to obtain the regional fairness contribution value for the currently calculated management region.

[0038] Specifically, the formula for calculating the Shapley value can be as follows: ; in, Let be the Shapley value of managed region j, N be the set of all managed regions, and S be any subset excluding managed region j. For the alliance The payoff function, Indicates alliance The number of members.

[0039] Specifically, the payoff function The mathematical expression is: ; in, —The total carbon emission budget of each management region is the total carbon emission budget to be allocated; , The alliance Sum of all sets The total population; , The alliance Sum of all sets The sum of GDP; , The alliance Sum of all sets The total historical carbon emissions; , , —Normalized weighting coefficients, satisfying .

[0040] As a preferred embodiment, obtaining the regional efficiency index score for each management region based on the production efficiency data corresponding to each management region includes: Each time a calculation is performed, the production efficiency data for the current calculation is standardized to obtain the standardized production efficiency data for the current calculation. Based on the preset set of evaluation indicators, the entropy value calculation formula is used to calculate the weight of each evaluation indicator in the preset set of evaluation indicators for the current management area. By combining the currently calculated standardized production efficiency data with the weights of each indicator, the regional efficiency index score corresponding to the currently calculated management area is obtained.

[0041] Specifically, the process for scoring the regional efficiency index can be described as follows: ① There are n management areas, and m evaluation indicators are established to form the original data matrix. ; ② Calculate the first using the following formula The first evaluation indicator Weight of each enterprise: ; ; in, For the i-th sample, the raw data on the j-th evaluation metric. Let j be the minimum value of the j-th evaluation index among all samples. Let j be the maximum value of the j-th evaluation index among all samples. For the standardized data of the i-th sample on the j-th evaluation index, Let represent the standardized percentage of the i-th sample on the j-th evaluation indicator, and n be the number of management areas.

[0042] ③ Determine the first number using the following formula The first management area Entropy value of the indicator: ; in, For the first The first management area The entropy value of the indicator.

[0043] ④No. Coefficient of difference of the items Calculation formula: ; ⑤No. The formula for calculating the objective weight of each indicator is as follows: ; ⑥No. Formula for calculating the efficiency index score of each management area: ; S3. Based on the fairness contribution value and efficiency index score of each region, a multi-objective optimization model is established with the maximization of the weighted sum of the fairness contribution value and the efficiency index score as the optimization objective. The multi-objective optimization model is then solved to obtain the initial carbon emission budget allocation ratio corresponding to each management region. As a preferred implementation, the step of establishing a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the regional efficiency index score as the optimization objective, includes: Based on the fairness contribution value and efficiency index score of each region, an objective function is established with the goal of maximizing the weighted sum of Shapley value weight and entropy value weight. Based on the fairness contribution value of each region and the efficiency index score of each region, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value and minimum entropy. The multi-objective optimization model is established based on the objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, and the minimum entropy constraint.

[0044] Specifically, the process of establishing the multi-objective optimization model can be described as follows: ① Objective function ; ②Constraints Non-negativity constraint on weights: ; Weight sum constraint: , ; Weight upper and lower bound constraints: , ; Shapley value scaling constraint: , ; Minimum entropy constraint: , ; in, Total number of managed areas; ——No. The efficiency scores of each management region reflect its efficiency. ——No. The Shapley value for each management region reflects fairness; —The coefficients for the trade-off between fairness and efficiency in the objective function; , ——Optimal weight allocation The lower and upper limits (the upper limit is 0.2 and the lower limit is 0.01); , —The lower and upper limits of the Shapley value scaling factor (the upper limit is 2 and the lower limit is 0.5). —The minimum allowable entropy value of the system, with a reference value of 0.5; ——No. The allocation ratio of carbon emission budgets for each management area.

[0045] S4. Based on the production efficiency data, determine the comprehensive adjustment coefficient corresponding to each management area, and based on the real-time monitoring data, determine the carbon emission budget elasticity correction amount corresponding to each management area. As a preferred embodiment, determining the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data includes: The production efficiency data are divided into positive efficiency index data and negative efficiency index data. The positive efficiency index data is standardized to obtain positive standardized data, and the negative efficiency index data is standardized using a second preset standardization formula to obtain negative standardized data. Based on the weight of each indicator, the positive standardized data, and the negative standardized data, the comprehensive adjustment coefficient corresponding to each management region is determined.

[0046] Specifically, determining the comprehensive adjustment coefficient for each management area based on the production efficiency data can be implemented through the following preferred scheme: First, a dynamic adjustment and evaluation indicator system is constructed, comprising historical performance indicators and real-time performance indicators. The historical performance indicators include two specific metrics: the average annual decline rate of historical carbon emission intensity and the budget execution deviation rate of the previous cycle. A higher value for the former indicates stronger regional emission reduction efforts (a positive indicator), while a lower value for the latter indicates more precise budget execution (a negative indicator). The real-time performance indicators include two specific metrics: energy consumption per unit of GDP in the current period and the proportion of non-fossil energy consumption in the current period. A lower value for the former indicates higher energy efficiency (a negative indicator), while a higher value for the latter indicates a cleaner energy structure (a positive indicator). After obtaining the raw data for these four indicators in each management region, the Min-Max standardization method is used to perform dimensionless processing on the indicator data with different dimensions and directions. Subsequently, the entropy method is used to determine the objective weights of each indicator.

[0047] As a preferred implementation, determining the carbon emission budget flexibility adjustment amount corresponding to each of the management areas based on the real-time monitoring data includes: Based on the real-time monitoring data, the baseline carbon emissions, and the electricity carbon emission elasticity coefficients, the carbon emission budget elasticity adjustment amount corresponding to each management area is determined; wherein, the baseline carbon emissions are determined based on a preset carbon emission measurement device, and the electricity carbon emission elasticity coefficients are determined based on the baseline carbon emissions.

[0048] Specifically, determining the carbon emission budget flexibility adjustment amount for each management area based on the real-time monitoring data can be implemented through the following preferred schemes: First, a baseline scenario needs to be pre-defined, which includes the expected electricity consumption growth rate for each management region, serving as a reference for measuring the fluctuation range of actual electricity consumption. Based on this, a parameter reflecting the correlation between changes in electricity consumption and carbon emissions—the electricity carbon emission elasticity coefficient β—is calculated using historical data. Its mathematical expression is β=(ΔC / C0) / (ΔE / E0), where ΔC represents the change in regional carbon emissions, C0 represents the carbon emissions in the baseline period, ΔE represents the change in electricity consumption, and E0 represents the electricity consumption in the baseline period. The baseline carbon emissions C0 are obtained using pre-defined carbon emission measurement devices, such as continuous monitoring equipment installed at key emission sources or regional power grid nodes, to ensure the accuracy and authority of the baseline data.

[0049] During system operation, the actual electricity consumption in each management area is tracked in real time through a power monitoring system or a power data acquisition system. The actual electricity consumption growth rate is compared with the expected growth rate preset in the aforementioned baseline scenario, and the deviation ΔgE between the two is calculated. This deviation directly quantifies the magnitude of abnormal fluctuations in electricity consumption caused by uncertainties such as economic development, climate conditions, or energy structure adjustments. Finally, by substituting the baseline carbon emissions C0, the calculated electricity carbon emission elasticity coefficient β, and the real-time monitored deviation ΔgE of the electricity consumption growth rate into the correction formula, the elasticity correction amount for the carbon emission budget due to electricity consumption deviating from the expected level can be calculated. If the actual electricity consumption growth rate is higher than expected, the correction amount is positive, meaning that the budget needs to be increased to match the increase in energy activities; conversely, it is negative, requiring a budget reduction to reflect the contraction trend of actual carbon emissions. Thus, by organically linking the three links of determining the initial anchor point through a benchmark measurement device, fitting the elasticity coefficient with historical data, and capturing operational deviations through real-time monitoring, the precise quantification and dynamic correction of the impact of uncertainties in electricity consumption are achieved.

[0050] S5. Based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elasticity correction amount, obtain the carbon emission budget corresponding to each management area, and perform carbon emission scheduling for each carbon emission unit in each management area according to the carbon emission budget.

[0051] As a preferred implementation, obtaining the carbon emission budget corresponding to each management area based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elasticity adjustment amount includes: Each of the initial carbon emission budget allocation ratios is multiplied by the comprehensive adjustment coefficient to obtain the first multiplier value corresponding to each of the management areas. Each of the first multipliers is then multiplied by the carbon emission budget elasticity adjustment amount to obtain the carbon emission budget corresponding to each of the management areas.

[0052] In summary, this application's embodiments, by acquiring historical carbon emission data and production efficiency data, calculate fairness contribution values ​​and efficiency index scores using the Shapley value method and entropy value method respectively, achieving a quantitative separation of fairness and efficiency dimensions. This provides a dual benchmark for subsequent optimization, taking into account both historical responsibility and development weight, avoiding biases caused by single-indicator allocation. Secondly, a multi-objective optimization model is constructed with the goal of maximizing the weighted average of fairness and efficiency, and multiple constraints are introduced to ensure that the initial budget allocation ratio achieves a game equilibrium between fairness and efficiency while meeting regional carrying capacity, thus improving the scientific nature of the initial scheme. Based on this, according to production efficiency data... The comprehensive adjustment coefficient is calculated to incentivize or constrain the initial budget, dynamically linking the budget amount to the region's actual emission reduction efforts. Simultaneously, the flexible adjustment amount for the carbon emission budget is determined based on real-time monitoring data. By quantifying the correlation between electricity consumption fluctuations and carbon emissions, uncertainties in power system operation are incorporated into the adjustment scope, enabling the budget to dynamically adjust in response to changes in actual energy consumption scenarios. Finally, the initial allocation ratio, comprehensive adjustment coefficient, and flexible adjustment amount are integrated to generate the final carbon emission budget, which is then used to schedule each carbon emission unit. This achieves a shift from static quota allocation to dynamic flexible response, ensuring the accuracy of carbon emission scheduling from multiple dimensions.

[0053] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a carbon emission scheduling device, comprising: a data acquisition module 21, a first scheduling module 22, a second scheduling module 23, a third scheduling module 24, and a fourth scheduling module 25; Data acquisition module 21 is used to acquire real-time monitoring data corresponding to each management area in the city; The first scheduling module 22 is used to calculate the regional fairness contribution value of each management area based on the historical carbon emission data of each management area, and to obtain the regional efficiency index score of each management area based on the production efficiency data of each management area. The second scheduling module 23 is used to establish a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the efficiency index score of each region as the optimization objective, and solve the multi-objective optimization model to obtain the initial carbon emission budget allocation ratio corresponding to each management region. The third scheduling module 24 is used to determine the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data, and to determine the carbon emission budget elasticity correction amount corresponding to each management area based on the real-time monitoring data. The fourth scheduling module 25 is used to obtain the carbon emission budget corresponding to each management area based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elastic correction amount, and to perform carbon emission scheduling on each carbon emission unit in each management area according to the carbon emission budget.

[0054] As a preferred implementation, the step of calculating the regional equity contribution value corresponding to each management area based on the historical carbon emission data corresponding to each management area includes: Each management region is assigned a set of management regions. Based on the management regions and historical carbon emission data, the Shapley value calculation formula is used to calculate the regional fairness contribution value for each management region. In each calculation, the marginal contribution value of each subset in the management region set corresponding to the currently calculated management region is obtained based on the currently calculated management region and the historical carbon emission data corresponding to the currently calculated management region. The marginal contribution values ​​are then weighted and summed to obtain the regional fairness contribution value for the currently calculated management region.

[0055] As a preferred embodiment, obtaining the regional efficiency index score for each management region based on the production efficiency data corresponding to each management region includes: Each time a calculation is performed, the production efficiency data for the current calculation is standardized to obtain the standardized production efficiency data for the current calculation. Based on the preset set of evaluation indicators, the entropy value calculation formula is used to calculate the weight of each evaluation indicator in the preset set of evaluation indicators for the current management area. By combining the currently calculated standardized production efficiency data with the weights of each indicator, the regional efficiency index score corresponding to the currently calculated management area is obtained.

[0056] As a preferred embodiment, determining the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data includes: The production efficiency data are divided into positive efficiency index data and negative efficiency index data. The positive efficiency index data is standardized to obtain positive standardized data, and the negative efficiency index data is standardized using a second preset standardization formula to obtain negative standardized data. Based on the weight of each indicator, the positive standardized data, and the negative standardized data, the comprehensive adjustment coefficient corresponding to each management region is determined.

[0057] As a preferred implementation, the step of establishing a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the regional efficiency index score as the optimization objective, includes: Based on the fairness contribution value and efficiency index score of each region, an objective function is established with the goal of maximizing the weighted sum of Shapley value weight and entropy value weight. Based on the fairness contribution value of each region and the efficiency index score of each region, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value and minimum entropy. The multi-objective optimization model is established based on the objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, and the minimum entropy constraint.

[0058] As a preferred implementation, determining the carbon emission budget flexibility adjustment amount corresponding to each of the management areas based on the real-time monitoring data includes: Based on the real-time monitoring data, the baseline carbon emissions, and the electricity carbon emission elasticity coefficients, the carbon emission budget elasticity adjustment amount corresponding to each management area is determined; wherein, the baseline carbon emissions are determined based on a preset carbon emission measurement device, and the electricity carbon emission elasticity coefficients are determined based on the baseline carbon emissions.

[0059] As a preferred implementation, obtaining the carbon emission budget corresponding to each management area based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elasticity adjustment amount includes: Each of the initial carbon emission budget allocation ratios is multiplied by the comprehensive adjustment coefficient to obtain the first multiplier value corresponding to each of the management areas. Each of the first multipliers is then multiplied by the carbon emission budget elasticity adjustment amount to obtain the carbon emission budget corresponding to each of the management areas.

[0060] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0061] In summary, this application's embodiments, by acquiring historical carbon emission data and production efficiency data, calculate fairness contribution values ​​and efficiency index scores using the Shapley value method and entropy value method respectively, achieving a quantitative separation of fairness and efficiency dimensions. This provides a dual benchmark for subsequent optimization, taking into account both historical responsibility and development weight, avoiding biases caused by single-indicator allocation. Secondly, a multi-objective optimization model is constructed with the goal of maximizing the weighted average of fairness and efficiency, and multiple constraints are introduced to ensure that the initial budget allocation ratio achieves a game equilibrium between fairness and efficiency while meeting regional carrying capacity, thus improving the scientific nature of the initial scheme. Based on this, according to production efficiency data... The comprehensive adjustment coefficient is calculated to incentivize or constrain the initial budget, dynamically linking the budget amount to the region's actual emission reduction efforts. Simultaneously, the flexible adjustment amount for the carbon emission budget is determined based on real-time monitoring data. By quantifying the correlation between electricity consumption fluctuations and carbon emissions, uncertainties in power system operation are incorporated into the adjustment scope, enabling the budget to dynamically adjust in response to changes in actual energy consumption scenarios. Finally, the initial allocation ratio, comprehensive adjustment coefficient, and flexible adjustment amount are integrated to generate the final carbon emission budget, which is then used to schedule each carbon emission unit. This achieves a shift from static quota allocation to dynamic flexible response, ensuring the accuracy of carbon emission scheduling from multiple dimensions.

[0062] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0063] Example 3 Based on the above embodiments of the carbon emission scheduling method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the carbon emission scheduling method of any embodiment of the present invention.

[0064] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0065] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0066] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0067] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the carbon emission scheduling method described in any of the above-described method embodiments of the present invention.

[0068] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0069] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A carbon emission scheduling method, characterized in that, include: Obtain real-time monitoring data for each management area in the city; Based on the historical carbon emission data corresponding to each management area, the regional equity contribution value corresponding to each management area is calculated, and based on the production efficiency data corresponding to each management area, the regional efficiency index score corresponding to each management area is obtained. Based on the fairness contribution value and efficiency index score of each region, a multi-objective optimization model is established with the goal of maximizing the weighted sum of the fairness contribution value and the efficiency index score of each region. The multi-objective optimization model is then solved to obtain the initial carbon emission budget allocation ratio corresponding to each management region. Based on the production efficiency data, determine the comprehensive adjustment coefficient corresponding to each management area, and based on the real-time monitoring data, determine the carbon emission budget elasticity adjustment amount corresponding to each management area. Based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elasticity adjustment amount, the carbon emission budget corresponding to each management area is obtained, and carbon emission scheduling is carried out for each carbon emission unit in each management area according to the carbon emission budget.

2. The carbon emission scheduling method as described in claim 1, characterized in that, The step of calculating the regional equity contribution value for each management region based on historical carbon emission data for each management region includes: Each management region is assigned a set of management regions. Based on the management regions and historical carbon emission data, the Shapley value calculation formula is used to calculate the regional fairness contribution value for each management region. In each calculation, the marginal contribution value of each subset in the management region set corresponding to the currently calculated management region is obtained based on the currently calculated management region and the historical carbon emission data corresponding to the currently calculated management region. The marginal contribution values ​​are then weighted and summed to obtain the regional fairness contribution value for the currently calculated management region.

3. The carbon emission scheduling method as described in claim 1, characterized in that, The step of obtaining the regional efficiency index score for each management region based on the production efficiency data corresponding to each management region includes: Each time a calculation is performed, the production efficiency data for the current calculation is standardized to obtain the standardized production efficiency data for the current calculation. Based on the preset set of evaluation indicators, the entropy value calculation formula is used to calculate the weight of each evaluation indicator in the preset set of evaluation indicators for the current management area. By combining the currently calculated standardized production efficiency data with the weights of each indicator, the regional efficiency index score corresponding to the currently calculated management area is obtained.

4. The carbon emission scheduling method as described in claim 3, characterized in that, The step of determining the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data includes: The production efficiency data are divided into positive efficiency index data and negative efficiency index data. The positive efficiency index data is standardized to obtain positive standardized data, and the negative efficiency index data is standardized using a second preset standardization formula to obtain negative standardized data. Based on the weight of each indicator, the positive standardized data, and the negative standardized data, the comprehensive adjustment coefficient corresponding to each management region is determined.

5. The carbon emission scheduling method as described in claim 1, characterized in that, The step involves establishing a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the goal of maximizing the weighted sum of the fairness contribution value and the regional efficiency index score. The model includes: Based on the fairness contribution value and efficiency index score of each region, an objective function is established with the goal of maximizing the weighted sum of Shapley value weight and entropy value weight. Based on the fairness contribution value of each region and the efficiency index score of each region, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value and minimum entropy. The multi-objective optimization model is established based on the objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, and the minimum entropy constraint.

6. The carbon emission scheduling method as described in claim 1, characterized in that, The step of determining the carbon emission budget flexibility adjustment amount for each management area based on the real-time monitoring data includes: Based on the real-time monitoring data, the baseline carbon emissions, and the electricity carbon emission elasticity coefficients, the carbon emission budget elasticity adjustment amount corresponding to each management area is determined; wherein, the baseline carbon emissions are determined based on a preset carbon emission measurement device, and the electricity carbon emission elasticity coefficients are determined based on the baseline carbon emissions.

7. The carbon emission scheduling method as described in claim 1, characterized in that, The process of obtaining the carbon emission budget corresponding to each management region based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elasticity adjustment amount includes: Each of the initial carbon emission budget allocation ratios is multiplied by the comprehensive adjustment coefficient to obtain the first multiplier value corresponding to each of the management areas. Each of the first multipliers is then multiplied by the carbon emission budget elasticity adjustment amount to obtain the carbon emission budget corresponding to each of the management areas.

8. A carbon emission control device, characterized in that, include: The system comprises a data acquisition module, a first scheduling module, a second scheduling module, a third scheduling module, and a fourth scheduling module. The data acquisition module is used to acquire real-time monitoring data corresponding to each management area in the city; The first scheduling module is used to calculate the regional fairness contribution value of each management area based on the historical carbon emission data of each management area, and to obtain the regional efficiency index score of each management area based on the production efficiency data of each management area. The second scheduling module is used to establish a multi-objective optimization model based on the fairness contribution value and efficiency index score of each region, with the maximization of the weighted sum of the fairness contribution value and the efficiency index score of each region as the optimization objective, and solve the multi-objective optimization model to obtain the initial carbon emission budget allocation ratio corresponding to each management region. The third scheduling module is used to determine the comprehensive adjustment coefficient corresponding to each management area based on the production efficiency data, and to determine the carbon emission budget elasticity correction amount corresponding to each management area based on the real-time monitoring data. The fourth scheduling module is used to obtain the carbon emission budget corresponding to each management area based on the initial carbon emission budget allocation ratio, the comprehensive adjustment coefficient, and the carbon emission budget elastic correction amount, and to perform carbon emission scheduling on each carbon emission unit in each management area according to the carbon emission budget.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the carbon emission scheduling method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the carbon emission scheduling method as described in any one of claims 1-7.

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